sharing knowledge to kms

IBIMA Publishing

Journal of Organizational Knowledge Management

http://www.ibimapublishing.com/journals/JOKM/jokm.html

Vol. 2010 (2010), Article ID 325835, 12 pages

Copyright © 2010 Kamla Ali Al-Busaidi, Lorne Olfman, Terry Ryan, and Gondy Leroy. This is an open access

article distributed under the Creative Commons Attribution License unported 3.0, which permits unrestricted

use, distribution, and reproduction in any medium, provided that original work is properly cited. Contact

Author: Kamla Ali Al-Busaidi, e-mail: [email protected]

Sharing Knowledge to A Knowledge

Management System: Examining the

motivators and the benefits in an

Omani organization

Kamla Ali Al-Busaidi1, Lorne Olfman

2, Terry Ryan

2, and Gondy Leroy

2

1Sultan Qaboos University, AlKhod, Oman

2Claremont Graduate University, Claremont, USA

_________________________________________________________________________________

Abstract

Knowledge is a powerful resource that enables individuals and organizations to achieve several benefits such as improved learning and decision-making. Repository knowledge management system (KMS) assists organizations to efficiently capture their knowledge for later reuse. However, the breadth and depth of a knowledge management system depends on the magnitude of knowledge contributed to the system. This paper empirically investigated the motivators of individual knowledge sharing behavior and the individual benefits of such behavior. Data was collected through a questionnaire from 104 employees in a major private petroleum organization in Oman and analyzed by the partial least square analysis methodology. The results suggested that an individual's knowledge sharing behavior to KMS was motivated by organizational-culture dimensions (such as management support and rewards policy) and the system technical characteristics (such as system quality). Information technology service quality and peers trustworthiness were not significant motivators on individual knowledge sharing behavior. The results also suggested that individuals gain several benefits from sharing their knowledge to a repository KMS. The study provided implications for researchers and practitioners of KMS.

Keywords: Knowledge Sharing; Knowledge Management Systems; Knowledge Sharing Motivators;

Knowledge Sharing Benefits _________________________________________________________________________________

Introduction

Knowledge is a powerful asset; knowledge

can be codified, manipulated and

communicated. Organizations can achieve

several benefits through knowledge

management (KM) (Davenport and Prusak,

1998). The power and benefits of knowledge

and its management can be realized through

individual and organizational learning

processes. Knowledge management has

become one of the main imperatives of the

information age economy (Alavi and Leidner,

2001). Knowledge management systems

(KMS) are information systems that are

developed to boost the effectiveness of the

organization’s knowledge management.

The breadth and depth of a knowledge

management system (KMS) depends on the

magnitude of knowledge contributed to the

system. Thus, knowledge contribution

(sharing) is a critical KM process. Without

the codified knowledge, KMS cannot operate.

Therefore examining the factors that affect

the individual knowledge sharing behavior is

Journal of Organizational Knowledge Management 2

essential to the success of the deployment of

organizational KMS. Individual experts spend

the time and efforts to create explicit

knowledge and store it on a knowledge

repository (organizational memory) for

future organizational reuse. However, limited

studies have focused on individual KMS use

(such as knowledge contribution)

(Kankanhalli and Tan, 2004). Moreover, the

cultural aspect is a key ingredient to the

success of KMS (Davenport and Prusak,

1998; O’Dell and Grayson, 1998; Scholl, et al.,

2004). Thus, an integration of social and

technical dimensions is crucial for this KMS

investigation.

Persuading individuals to contribute their

knowledge to organizational repository KMS

is even more challenging in an Arabian

Culture such as Oman. In the Arab culture,

knowledge is generally perceived as power

and private. Thus, they will most likely feel

reluctant to share their knowledge (power)

with others, because they might loose their

value and competitive advantage.

Nevertheless, the deployment of KMS is very

essential for developing countries to

efficiently manage their knowledge and build

their human resources (World Bank., 2003).

Thus, developing a knowledge-culture is very

crucial to promote individuals’ knowledge

sharing behavior and consequently have a

successful KMS deployment in these

countries. Very limited study investigated

the determinants of a successful KMS

deployment in the Middle East and Oman

specifically. Little research, however,

indicated the deployment of organizational

KMS requires combination of technical and

social (organizational culture) factors

(Ahmed and Hegazy, 2006; Al-Busaidi and

Olfman, 2005; Al-Athari and Zairi , 2001). In

a qualitative exploratory study, Al-Busaidi et

al.(2007) revealed some determinants of

knowledge sharing and knowledge

utilization behaviors. This study took a solid

empirical observation specifically at the

motivators and benefits of individual

knowledge sharing behavior in Oman.

Consequently, the main objective of this

paper was to empirically examine the social

and technical factors that affect the

individual knowledge sharing behavior to

repository KMS. It specifically investigated

the effects of system’s quality, service quality,

management support, rewards policy and

peers trustworthiness on knowledge sharing.

It also examined the benefits that individuals

gain from sharing and codifying their

knowledge to a repository KMS.

The next section discusses the background

literature of knowledge sharing process, the

determinants of knowledge sharing behavior

and the benefits of knowledge sharing. The

literature section is followed by the study

framework and hypotheses, methodology,

analysis and conclusion sections respectively.

Background Literature

Knowledge Sharing Process

Knowledge sharing is the sharing of one’s

own knowledge to other individuals; it is one

of major organizational KMS processes

(Becerra-Fernandez et al., 2004). Knowledge

sharing through a repository KMS involves

what Alavi and Leidner (2001) refers to as

codification and storage process, the process

of storing the explicit knowledge for later

use.

Repository KMS is one of two traditional

approaches, the most popular one, for the

development of organizational KMS, along

with the network model (Alavi and Leidner,

2001; Davenport and Prusak, 1998 ). The aim

of this approach is to codify the

organization’s explicit knowledge to create

an organizational memory. The development

of a repository KMS offers several

advantages for organizations (Alavi and

Leidner, 2001). It helps in establishing

“organization memory” (OM): general,

explicit and articulated knowledge of the

organization. Accordingly, it helps in

3 Journal of Organizational Knowledge Management

efficiently storing and reapplying workable

solutions. Repository KMS also speed up and

broaden the traditional knowledge sharing

for socializing newcomers, that is, the

transmission of the cultural rituals and

routines (Davenport and Prusak, 1998). This

is along with several direct and indirect

organizational benefits.

However, the value of the repository KMS

depends on the amount and the quality of

knowledge that is stored in it. As a behavior,

knowledge sharing may be deterred by

several social inhibitors. These main social

inhibitors of knowledge sharing are fear of

(1) losing value (power), (2) losing work

time (cost), and (3) misinterpretation of the

shared knowledge (Davenport and Prusak,

1998; Husted and Michailova, 2002; O’Dell

and Grayson, 1998). Individuals feel that they

lose their competitive advantage when they

share their expertise with others. They also

feel that knowledge sharing will cost them a

lot of time that they would rather spend on

personal work. Also, individuals may fear

that their peers who might utilize their

knowledge may misinterpret the shared

knowledge and that may cause bad work

consequences. At a technical level,

knowledge contribution involves the task of

storing/uploading knowledge to repository

KMS (Maier, 2002). Thus, a good system

quality with an effective and efficient

storage/upload function is critical for

individuals’ knowledge contribution.

Little research investigates knowledge

sharing as a measurement of KMS usage. For

example, Marks (2001) measured knowledge

sharing by: (1) frequency of contribution,

and (2) efforts to contribute knowledge that

has positive value for the organization. Maier

(2002) proposed that knowledge-publication

might be measured by number/size of

knowledge elements published per topic. To

avoid the problem resulting from using self-

reported objective measures, in this paper,

knowledge contribution is measured by

users’ perceptions of the extent to which they

contribute/upload knowledge to the

repository KMS.

Determinants of Knowledge Sharing

Generally, an effective deployment of a KMS

requires several factors. There are several

technical and social factors that influence the

knowledge sharing behavior. Based on

DeLone and McLean's 2003 IS Success Model,

the technical factors that affect any

information system use are related to

information quality, system quality and

service quality. Information (or knowledge)

quality is critical only for knowledge

utilization not knowledge sharing behavior.

For knowledge sharing and codification,

system quality refers to the quality of the

system storage/upload function.

Based on the management and IS literature,

organizational culture (Social factors) is very

crucial on knowledge management.

Corporate culture plays a key role in the

success of KMS. Culture is defined as the

shared values, beliefs and practices of the

people in the organization (Schein ,1985).

Culture values form an organization’s norms

and practices, which consequently control

employees’ behaviors such as knowledge

sharing (De Long and Fahey, 2000).

Several dimensions of knowledge culture

have been highlighted by several theoretical

and qualitative studies (Al-Busaidi et al.,

2007; De Long and Fahey, 2000; Krogh,

1998; O’Dell and Grayson, 1998). The most

cited social dimensions are management

support, rewards policy, and trust. Few KMS

studies have included a cultural construct in

their model. This study aimed to provide

better understanding of the dimensions of

KMS culture that motivate individuals’

knowledge contribution to a repository KMS.

It specifically investigates the effects of

management support, rewards policy, and

peers trustworthiness on the individual’s

knowledge sharing behavior. Management

support is very important to clarify and

acknowledge the importance of KMS,

knowledge sharing to the organization’s

success. Management support is also


important to provide individuals time to

share and codify knowledge. Rewards policy

is another important factor that motivates

KMS users to spend time and efforts to

contribute knowledge to the KMS (O’Dell and

Grayson, 1998). Peers-trustworthiness

motivates knowledge contributors to share

knowledge (Davenport and Prusak, 1998).

More discussion on these factors is provided

in the hypotheses section.

Benefits of Knowledge Sharing

Based on DeLone and McLean’s (2003)

model of IS success, the IS use may result in

net benefits (an individual and organizational

benefits). This paper investigated the

individual benefits. There are several

individual benefits that may result from

knowledge sharing behavior (Hendriks,

1998; Maier, 2002). Based on Herzberg’s two

factors theory, Hendriks(1998) argued that

individuals share knowledge because of

motivation factors rather than hygiene

factors. Motivation factors are related to

achievement, responsibility, recognition,

work-challenge, and operational autonomy.

Hygiene factors are salary, bonuses and

penalties. KMS also improves individuals’

performance and productivity in terms of

time and speed of the knowledge sharing

process (Maier, 2002). These all cited

benefits may be classified as tangible,

intangible and performance benefits.

Study Framework & Hypotheses

Study framework

This study investigated the motivators and

benefits of the individual’s knowledge

sharing to a repository KMS. It empirically

examined the effects of the system quality,

service quality, management support,

rewards policy and peers trustworthiness on

the knowledge sharing behavior to a

repository KMS. Figure 1 illustrates this

study framework.

Fig. 1: The Study Framework

System Quality

System quality refers to the ease, speed,

completeness, and effectiveness of the

storage/upload function of the KMS. As for

knowledge sharing and codification, it is very

important to have a KMS structure that

enables faster and easier codification of

knowledge (Alavi and Leidner, 2001;

Davenport and Prusak, 1998 ). Advanced


storage and retrieval tools can effectively

enhance organizational memory, repository

KMS (Alavi and Leidner, 2001). In a

qualitative study, the ease of storage found to

encourage people to contribute knowledge

(Goodman and Darr, 1998). Likewise, Al-

Busaidi et al.(2007) in a qualitative study

found that system quality in terms of ease of

use , speed and integration is critical for

knowledge sharing behavior. Thus, we

hypothesize the following:

Hypothesis (1): Higher system quality

improves knowledge sharing to a repository

KMS.

Service Quality

Service quality involves the quality of IS staff

support to the system’s end-users. It is

assessed here by the five indicators:

reliability, responsiveness, assurance, and

empathy (based on Kettinger and

Lee(1994)), and training. Users of any

system have similar criteria for evaluating

service quality (Parasuraman et al, 1985). IS

effectiveness measurement is undermined by

ignoring service quality (DeLone and

McLean, 2003). For effective KMS

deployment, service quality is also important

(Maier, 2002). Reliable. Responsive,

understandable, and available IT support

staff is essential to motivate KMS users. Also,

training is needed to improve the success of

an information system (Turban et al., 2001).

Thus, we hypothesize the following:

Hypothesis (2): Higher service quality


KMS.

Management Support

Management support here refers to clarifying

the goal, vision and importance of a KMS, and

encouraging end-users (Davenport and

Prusak, 1998; Gold et al., 2001).

Management’s open approval and

acknowledgement of knowledge exchange

reduces individual experts’ fear of losing

their values. Also, providing employees the

time to share knowledge encourages them to

spend them to make an effort to do so.

Management support is extremely critical to

endorse the KMS and consequently change

employees’ attitudes. In the Arab culture,

managers are recognized as high authority

(Ali, 1990) and their support for KMS

projects, which are emerging systems,

certainly enhances employees’ confidence to

share their knowledge through the system

for organizational problem solving and

decisions making. Management support was

also cited as a social determinant of

knowledge sharing in Al-Busaidi et

al.'s(2007) qualitative study. Therefore, the

following hypothesis is proposed:

Hypothesis (3): Higher management support


KMS.

Rewards Policy

Rewards are “non trivial” monetary and non-

monetary incentives. Rewards policy is a

critical factor for KMS especially for

knowledge sharing because the breadth and

depth of a KMS project is based on the

participation of the employees to create and

codify their knowledge in these systems for

others’ use. It encourages employees to

spend time and make the effort to create and

codify their explicit knowledge (Davenport

and Prusak, 1998). Without good incentives

employees will be reluctant to exchange and

contribute their own knowledge to the KMS

(O’Dell and Grayson, 1998). Therefore:

Hypothesis (4): More effective reward policy


KMS.

Peers Trustworthiness

Trust is defined as a set of mutual

expectations shared by people involved in

collaboration and exchange (Zucker, 1986); it

is considered as a critical factor for

knowledge exchange. In terms of knowledge

sharing, trust is referred to as the


trustworthiness of the knowledge utilizers.

Knowledge sharing or “selling” in an

organization depends on the trustworthiness

of the knowledge utilizers (or buyers)

(Davenport and Prusak, 1998) ; if the

knowledge buyers do not give credit to the

knowledge sellers, and pretend that the

knowledge is theirs; then knowledge sellers

gain nothing. Thus, peers-trustworthiness

reduces knowledge owners’ fears, and

encourages them to share. The significance of

trust in several knowledge activities

including knowledge externalization was

found to be empirically significant (Lee, and

Choi, 2003). Consequently, the following

hypotheses are proposed:

Hypothesis (5): Peers trustworthiness


KMS.

Individual Benefits

As indicated earlier, there are several

benefits individuals may gain from

contributing their knowledge to a repository

KMS (Hendriks, 1999 ; Maier, 2002). These

benefits are related to tangible benefits such

as long-term salary increment or promotions,

intangible benefits such as reputation, and

autonomy and performance benefits such as

more efficient and faster knowledge sharing

process. Likewise, in Al-Busaidi et

al.'s(2007) qualitative study, knowledge

owners highlighted some benefits of

knowledge sharing Consequently:

Hypothesis (6): Higher knowledge sharing to a

repository KMS results in higher individual

benefits.

Study Methodology

Participants

This study's sample includes 104 employees

in a major private petroleum company in

Oman. The company accounts for about 90%

of the country’s crude-oil production and

nearly all of its natural-gas supply. Oil is the

major industry in Oman. Based on 2005

statistics published on the company’s

website, most of the employees (3784 staff)

of the company are local, which represent

82% of the total employees in the company.

The sample included KMS users of a specific

organizational knowledge management

system in this organization. The organization

developed this KMS because of business,

technological and cultural factors. The

objective of the organization is to enhance

the transparency and the accessibility of the

organization’s information and knowledge

throughout the organization, so employees

are able to access it from anywhere. The

system is a mean to transfer

information/knowledge within one

department or across departments. For

example, petroleum engineers across several

oil fields can use the system to share or

locate common problems’ solutions. Also

information/knowledge can be shared across

several departments such as between

personnel and finance departments or

drilling department and geophysicists or

petroleum engineers.

Based on the IT department representatives,

this investigated system is a web-centric

application, with strong integration with the

MS-Office suite and mail. It provides

employees to store search and retrieve

organizational documents, information and

knowledge. Any employees in the

organization can voluntarily access the

system from the organization’s web home

page. However, limited number of employees

can contribute (or store) knowledge to the

system. These 104 participants represent

KMS users who are authorized to contribute

(codify) knowledge to the system. The 104

sample-size satisfies the partial least square

(PLS) analysis methodology sample

requirement.


Study Design

Data was collected through a survey

questionnaire of the perception of the

employees; the questionnaire was filled in

through electronic means (a web-site or by

filling out an electronic MS-word format

copy). The study sample was invited through

email by an official contact person

(established from a prior investigation) in

the human resources department at the

participating organization. Based on the

contact person’s suggestion, the applicable

sample was randomly selected from the

organization’s email lists. The study was

conducted in English (the typical medium of

business activities in Oman).

Questionnaire

The questionnaire contained the constructs

to be measured for quantitative analysis,

along with 10 demographic questions (e.g.,

gender, age, degree, KMS experience, work

experience, and job function). Construct

measurements items were phrased according

to a 7–point Likert scale. For the study’s

independent constructs, the scale was

defined as follows: 1= strongly disagree, 2=

disagree, 3= somewhat disagree, 4= neither

agree nor disagree, 5= somewhat agree, 6=

agree, 7= strongly agree. For the dependent

constructs, the scale is defined as follows: 1=

Never, 2= Very infrequently, 3= infrequently,

4= Sometimes, 5= frequently, 6= Very

frequently, 7= Always. A “Not applicable”

option was also given for all constructs to

ensure that individuals’ ratings are valid

responses.

The questionnaire included 33 indicators to

examine this study’s theoretical model. Some

of the measurements were based on previous

studies; for instance, system quality was

modified from on DeLone and McLean(2003)

and service quality was modified from

Kettinger and Lee(1994). The new self-

constructed measurements were developed

based on the relevant literature by the

method proposed by Moore and Benbasat

(1991). New self-constructed measurements

are management support, rewards policy,

peers trustworthiness, knowledge sharing

and individual benefits

Data Analysis and Findings

PLS Analysis Methodology

Data was analyzed by the PLS-Graph 3.0

software. PLS is a variance-based structural

equation model that allows path analysis of

models with latent variables. In PLS, a

distinction should be made whether the

indicators are reflective or formative (Chin,

1998). Reflective indicators measure the

same aspect of the underlying latent

construct, whereas the formative indicators

measure several aspects of their related

latent construct. Each indicator may be

correlated with the latent construct but not

necessarily with other indicators in their

block. In this study, indicators were

considered formative because they measure

several aspects of the underlying construct.

Sample Profile

Most of participants were males; female

represents only 20%. Around 97% were at

least 26 years old. About 86% had at least

two years of KMS-use experience. The

majority of the participants, 73%, were

Omani. About 56% of the participants were

group leaders, project managers or

department heads. About 50% of the

participants were engineers; 19% were

analysts; and 13% were consultants. Four

percent of respondents had PhD, 25% had

Master degree, 10% had postgraduate

diploma, 51% had Bachelors degree, and

10% had diploma. Table 1 shows a summary

of this profile.


Table 1: Sample Profile

Reliability and validity

With PLS, the reliabilities of the

measurements were evaluated through

internal consistency reliability, and the

validity was measured by the average

variance extracted (AVE), which refers to the

amount of variance a latent variable,

captures from its indicators. The

recommended level for internal consistency

reliability is at least 0.70, while for AVE, it is

at least 0.50 (Chin, 1998). Table 2 shows that

the study constructs’ reliability and AVE are

above the recommended levels.

Model Evaluation and Hypotheses Testing

With PLS the R-square values are used to

evaluate the predictive relevance of a structural

model for the dependent latent variable, and the

paths coefficients are used to assess the effects

of the independent variables. The model

hypotheses were tested by T-tests.

Bootstrapping technique was utilized with a re-

sampling of 200 to test the significance of the

PLS estimates of path coefficients. Based on

PLS-Graph user’s guide, this resample size

provides reasonable standard error estimates.

QUESTION %

Gender

Female 20%.

Male 80%

KMS Experience

>= 2 years 86%

< 2 years 14%

Nationality

Omani 73%,

NonOmani 27%

Job Position

Engineers 50%

Analysts 19%

Consultants 13%

Others 18%

Education

PhD 4%

Master 25%

Postgraduate diploma 10%

Bachelors 51%

Diploma 10%

Construct Total Items Reliability AVE

Management Support 4 0.926 0.760

System Quality 3 0.924 0.806

Service Quality 5 0.940 0.757

Rewards Policy 2 0.949 0.902

Peers Trustworthiness 4 0.943 0.806

Knowledge Sharing 5 0.876 0.587

Individual Benefits 10 0.936 0.598

Table 2: Constructs’ Reliability & AVE


Table 3 shows that R-squares for the

dependent variables knowledge sharing

process and individual benefits are 0.397 and

0.330, respectively. Thus, knowledge sharing

to repository KMS was 39.7%% determined

by its predictors (system quality, service

quality, management support, rewards

policy, and peers trustworthiness), while

individual benefits were 33% determined by

its predictor (knowledge contribution). Also,

the table shows that reward policy (β=0.290;

p = 0.1), management support (0.233; 0.1),

and system quality (0.224; 0.1) were the only

significant factors on knowledge sharing

behavior. Service quality and peers

trustworthiness were not significant

predictors of knowledge sharing behavior.

Knowledge sharing to repository KMS was

also found to significantly result in individual

contribution benefits (0.574; 0.005).

Thus, hypotheses H1 (storage level), H3

(management support), H4 (rewards policy),

and H6 (individual benefits) were supported,

but hypotheses H2 (service quality), and H5

(peers trustworthiness) were not supported.

Table 3: Model Evaluation Measures

Construct Mean R-Square Path coefficient

(β)

Sig. level (α)

Storage Level 1.88 NA 0.224 0.1

Service Quality 4.25 NA 0.126 NS

Management Support 4.41 NA 0.233 0.1

Peers Trustworthiness 4.61 NA 0.021 NS

Rewards Policy 2.30 NA 0.290 0.1

Knowledge Sharing 2.56 0.397 0.574 0.005

Individual Benefits 0.330 NA NA

NS = Not Significant;; NA = Not Applicable

Conclusion

Overview

This study mainly aimed to investigate the

factors that determine the individual

knowledge sharing behavior to a repository

KMS. It also evaluated the individual benefits

that gained from such behavior. A

questionnaire with quantitative indicators

was utilized for this investigation. PLS

methodology was utilized for the

quantitative analysis. The study was

conducted in Oman, a developing country.

KMS offers developing countries an effective

and efficient way to build their human

resources and consequently prepare them

for a knowledge-based economy. However,

knowledge in Arabian culture is considered

private and power, hence promoting a

knowledge behavior is even more

challenging in Arabian countries. This

investigation provided practitioners and

researchers some insights on the motivators

of knowledge sharing behavior and

consequently the success of KMS

deployment.

The results of this study showed that the

factors that significantly affected knowledge

sharing were, in order of their contributions,

rewards policy (β=0.290; p = 0.1),

management support (0.233; 0.1), and

system quality (0.224; 0.1). Service quality

(β = 0.126), and peers trustworthiness

(0.021) were found to be insignificant. This

indicates that the most important issue for

sharing knowledge to the repository KMS is

the rewards policy. Individuals freely spend

their time and effort to share their


knowledge (power) with others through the

KMS without any essential value added to

their own job. Thus, rewards policy is critical

in motivating them along with the support of

management in terms of encouragement and

time giving. It seems that once managers

support and rewards the knowledge

contributors, peers trustworthiness is not a

significant factor. Besides, the development

of a high quality of the system storage

function is crucial for the knowledge

contributors to have an easy and quick

sharing process,

This study also empirically detected

significant individual benefits resulting from

sharing knowledge to a repository KMS. A

higher knowledge sharing to the KMS results

in higher intangible benefits, sharing-

performance, and tangible benefits. Sharing

knowledge to the KMS improves an

individual’s reputation, work status and

performance, and experience of sharing

knowledge.

This study showed that the development of a

knowledge-oriented culture is very

significant on the success of KMS use

consistent with a number of studies in

developing countries such as (Al-Busaidi and

Olfman, 2005; Al-Athari and Zairi , 2001;

Syed-Ikhsan and Rowland, 2004). The

significance of management support on the

success of IT deployment was highly

supported by several studies from Arab

countries such as (Ahmed and Hegazy, 2006;

Khalfan and Alshawaf, 2004). The

significance of management support is also

consistent to an earlier study conducted by

Al-Busaidi and Olfman(2005) on the KMS

success factors in Omani organizations from

the IT managers’ perspective. However, this

study showed that individual knowledge

owners consider rewards policy as a valuable

strategy unlike the IT managers in the earlier

study. The significance of rewards policy is

also consistent with a study conducted in

Malaysian context (Yahya and Goh, 2002).

This study showed that organizational-

culture dimensions are more significant on

individual's knowledge sharing behavior

than the system dimensions consistent with

an earlier qualitative study conducted by Al-

Busaidi et al (2007).

Limitations and Future Research

This study had some limitations. First this

study was limited only to the repository

model of KMS. Second, the study was

investigated in one company and in one

country with a specific KMS. The benefit of

focusing on one organization and one KMS

was control. Of course, this limited its

generalization. Thus future research may

carry out this investigation in a network

model of KMS. Second, the study might be

investigated in different organizations and in

different culture and with different systems

to generalize the results. Third, future

research may also refine these study

measurements and develop new one to

strengthen the findings. Fourth, future

researchers may also conduct this

investigation through longitudinal study to

understand whether knowledge sharing

behavior is improved by the independent

variables suggested in this study and/or by

the benefits achieved through knowledge

sharing.

Implications for Practice

This study offered several implications for

research and practice. For practitioners, this

study indicated that knowledge management

is a socio-technical process; thus, the

development of a knowledge-based culture

and high quality system functionality are

essential for the success of knowledge

sharing process and consequently the

organizational KMS. Management support is

crucial to clarify the objective of KMS,

encourage end users, and most importantly

provide individuals the sufficient time to

create and codify knowledge. The

development of a rewards policy might be

vital for knowledge sharing. The study also

showed that deploying KMS provides

knowledge contributors some individual


benefits, which consequently may lead to

organizational benefits.

Acknowledgement

We would like to greatly thank the

participating company and research

participants.

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